Lithium Battery Analysis: Probability of Failure Assessment Using Logistic Regression

Overview

Fourteen-hundred rows by 53 columns of vendor cell acceptance data were processed though Logistic Regression using SAS Enterprise Miner (EM) to find any significant correlation between 52 test output parameters (independent variables) and the pass/fail outcome for each of the 1,400 battery cells tested. The goal was to find helpful predictors for detecting "Good" or "Bad" cells in the form of a best Logistic Regression model. Data from five cells selected by Johnson Space Center's (JSC's) Energy Systems Division (ESD) were processed through three model options (Option1, Option2, and Option3) to determine the best model and to indicate a known cell that failed. The output from the best model showed good acceptability statistics and indicated the failed cell was less acceptable than the other cells.